Ship every product decision with evidence behind it.
Run experiments, manage feature flags, and let your data warehouse do the heavy lifting—with statistical defaults built on 15 years of operating evidence. Proven at Spotify. Now yours.
Everything you need
to experiment and grow
Watches sessions, experiments, tells you what's next.
Battle-tested for the last decade at Spotify
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See the latest from Confidence
The rise of the product builder
As AI increases individual leverage across the product development stack, the boundaries between PM, design, analysis, and engineering are blurring.
42% of Spotify experiments get rolled back. That's the point.
At Spotify, 42% of experiments are rolled back after guardrail metrics detect regressions. The discipline to discard is more valuable than the ability to ship.
The product loop
Discovery and delivery are no longer sequential phases. AI collapsed the build phase, and now the teams winning are the ones with the tightest loops.
Accurate Sample Size for Always-Valid Inference
Most A/B testing tools either overestimate the sample size needed for always-valid inference or do not adjust for the sequential test in the sample size calculation at all. We derived a closed-form correction that requires no simulation.
When A/B tests tell you what you want to hear
A/B testing maturity has a dangerous middle phase: teams test, dislike the answer, and explain it away. How to spot it and learn to trust your data.